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An evaluation of sound event classification in an active learning setting: Support Vector Machines versus Random Forest Classification
University of Skövde, School of Informatics.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The process of labeling data can be work intensive, not least in the area of sound event classification. Active learning can ease this intensive process and still provide an acceptable performance by a classifier. In this thesis, the performance of the classification of sound events in an active learning setting is evaluated in terms of robustness w.r.t. bias in the data. An active learning algorithm for evaluating sound event data is built and an experiment is performed. The result is that with the used feature extraction method, there is no difference in performance between when active learning is used and when active learning is not used. Also, there is no difference between the sample selection methods random, margin and entropy selection in this scenario.

Place, publisher, year, edition, pages
2019. , p. 21
Keywords [en]
active learning, sound event classification, robustness, biased data, SVM, random forest
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:his:diva-17862OAI: oai:DiVA.org:his-17862DiVA, id: diva2:1376155
Subject / course
Informationsteknologi
Educational program
Data Science - Master’s Programme
Supervisors
Examiners
Available from: 2020-11-25 Created: 2019-12-08 Last updated: 2020-11-25Bibliographically approved

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